Data Science Class 10 Syllabus

The syllabus consists of five units: (i) Use of statistics in Data Science (ii) Distributions in Data Science (iii) Identifying Patterns (v) Data Merging (v) Ethics in Data Science.

1. Use of statistics in Data Science

  1. Introduction
  2. What are subsets?
  3. Two-way frequency table
  4. Interpreting two-way tables
  5. Two-way relative frequency table
  6. Meaning of mean
  7. Median
  8. Mean Absolute Deviation
  9. What is Standard Deviation?

2. Distributions in Data Science

  1. Introduction
  2. What is distribution in data science?
  3. What are different types of distributions?
  4. Statistical Problem Solving Process

3. Identifying Patterns

  1. What is partiality, preference and prejudice?
  2. How to identify the partiality, preference and prejudice?
  3. Probability for Statistics
  4. The Central Limit Theorem
  5. Why is the Central Limit Theorem important?

4. Data Merging

  1. Overview of Data Merging
  2. What is Z-Score?
  3. How to calculate a Z-score?
  4. How to interpret the Z-score?
  5. Why is a Z-score so important?
  6. Concept of Percentiles
  7. Quartiles
  8. Deciles

5. Ethics in Data Science

  1. Note about data governance framework
  2. Ethical guidelines around data analysis
  3. Discarding the Data
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